An environment for topology analysis and data reconciliation of the pre-heat train in an industrial refinery

Chebeir, Jorge; Webb, Zachary; Romagnoli, José A.

doi:10.1016/j.applthermaleng.2018.10.081

Cited by 10 publications

(1 citation statement)

References 26 publications

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“…Inspired by Kuehn's work, a lot of diversified data reconciliation models have emerged. From the early linear data reconciliation to the bilinear data reconciliation research, it gradually develops to the nonlinear data reconciliation for more and more complex research object [39,40]. In fact, data redundancy is a significant prerequisite for data reconciliation.…”

Section: A Proposed Generic Dr Modelmentioning

confidence: 99%

Energy Efficiency Analysis and Optimization of Industrial Processes Based on a Novel Data Reconciliation

2021

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The energy management and energy efficiency optimization are of particularly significant for promoting the sustainable development of industrial processes. However, industrial raw data with uncertain, relevant and inaccuracy characteristics influence the reliability and accuracy of energy efficiency analysis and optimization modeling. Therefore, an energy efficiency analysis and optimization method based on a novel data reconciliation (DR) integrating Gaussian mixture model (GMM) and mutual information (MI) is put forward. First, the material flow information with multiple data characteristics corresponding operation modes is divided through the GMM. Moreover, the novel data reconciliation model integrated with critical variable and mutual information is established considering time-scale redundancy information in different modes, then the comprehensive data reconciliation result is evaluated by the hypothesis testing. Furthermore, the reconciled data is applied to analyze the exergy balance and built the energy efficiency optimization model with multi-objective for a case study of industrial evaporation process. Finally, simulation case and industrial application case are used to analyze and discuss, and the results show that the validity and applicability of the proposed approach are illustrated in energy saving potential which is about 14.81%.INDEX TERMS Energy efficiency analysis, energy optimization, data reconciliation, Gaussian mixture model, mutual information, industrial processes.

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Section: A Proposed Generic Dr Modelmentioning

confidence: 99%